亚马逊Senior Data Engineer, Amazon Global Selling - AIT
任职要求
基本任职资格 - 5+ years of data engineering experience - Experience with data modeling, warehousing and building ETL pipelines - Experience with SQL - Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS - Experience mentoring team members on best practices 优先任职资格 - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR - Experience operating large data warehouses Our incl…
工作职责
• Design and implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, and storage, supporting both real-time and offline analytics needs. • Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance). • Collaborate with cross-functional teams (e.g., Product, Operations, Tech) to align data logic, integrate multi-source data (e.g., user behavior, transaction logs, AI outputs), and build a unified data layer. • Establish data standardization and governance policies to ensure consistency, accuracy, and compliance. • Provide structured data inputs for AI model training and inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows. • Explore innovative AI-data integration use cases (e.g., embedding AI-generated insights into BI tools). • Provide technical guidance and best practice on data architecture that meets both traditional reporting purpose and modern AI Agent requirements.
• Collaborate with BIE,DE, PM, CSM to research, design, develop, and evaluate generative AI solutions to address Global Selling challenges. • Interact with stakeholders directly to understand their business problems, aid them in implementation of generative AI solutions, brief stkaholders and guide them on adoption patterns and paths to production • Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
* Large-Scale Training Pipelines: Design and implement distributed training pipelines for LLMs using tools such as Fully Sharded Data Parallel (FSDP) and DeepSpeed, ensuring scalability and efficiency * LLM Customization & Fine-Tuning: Adapt LLMs for new languages, domains, and vision applications through continued pre-training, fine-tuning, and Reinforcement Learning with Human Feedback (RLHF) * Model Optimization on AWS Silicon: Optimize AI models for deployment on AWS Inferentia and Trainium, leveraging the AWS Neuron SDK and developing custom kernels for enhanced performance * Customer Collaboration: Interact with enterprise customers and foundational model providers to understand their business and technical challenges, co-developing tailored generative AI solutions
• Work closely with Senior MPE team members in supplier monitoring and reporting open issues • Participate in the review and development of part/sub-assembly process flow, including process fixture concept and DFM • Assist in the organization of technical data documentation throughout the development cycle including FAI, CMK, CPK, GR&R, and DFM • Travel domestically and internationally to sites as projects required • Can work 5 days per week during summer holiday for at least 3 months duration • Is willing to work in Shenzhen
• Define and deliver large-scale BI solutions, including data modeling, KPI standardization, and pipeline automation. • Lead migration of legacy reporting into modern AWS QuickSight and other BI platforms. • Establish and maintain data foundations for social and marketing data, ensuring completeness, accuracy, and compliance. • Conduct advanced analytics, including user behavior modeling, segmentation, and marketing campaign measurement. • Generate actionable insights and recommendations to improve seller journeys, marketing effectiveness, and AI agent performance. • Collaborate with marketing operation team, data engineers, product managers, SDEs, financial analysts, and data scientists to design metrics and guide business decisions. • Drive best practices in operational excellence, data quality management, and data governance. A day in the life You will define marketing BI strategy, build scalable data models and generate actionable insights that drive product, marketing, and AI initiatives. You will partner with data engineering, product, and science teams to ensure high-quality, compliant, and scalable data foundations. Your work will directly influence AI-powered agents, seller engagement, and business growth.